U.S. patent number 11,185,282 [Application Number 16/303,570] was granted by the patent office on 2021-11-30 for system and method for monitoring and identifying posology efficacy for an an individual.
This patent grant is currently assigned to APPMED INC.. The grantee listed for this patent is APPMED INC.. Invention is credited to Louis-Paul Marin, Philippe Stenstrom.
United States Patent |
11,185,282 |
Stenstrom , et al. |
November 30, 2021 |
System and method for monitoring and identifying posology efficacy
for an an individual
Abstract
A system for monitoring and identifying the efficacy of posology
for a target individual having a health condition with respect to
administration of a therapeutic composition assigned thereto for
treatment comprises a user interface, a database and a controller
in communication with the user interface and the database. The
database includes information of health conditions, symptoms,
therapeutic compositions, and side effects. The controller
comprises a memory of computer implementable steps for receiving
information from the user interface regarding the target
individual's health conditions, symptoms, therapeutic compositions
and side effects assigned to the target individual; comparing the
received information to a match with the health conditions, the
therapeutic compositions, the symptoms and the side effects;
prompting and receiving user feedback; and determining whether the
therapeutic composition administered at a dosage should be modified
to be increased or decreased.
Inventors: |
Stenstrom; Philippe (Laval,
CA), Marin; Louis-Paul (Laval, CA) |
Applicant: |
Name |
City |
State |
Country |
Type |
APPMED INC. |
Laval |
N/A |
CA |
|
|
Assignee: |
APPMED INC. (Laval,
CA)
|
Family
ID: |
1000005967134 |
Appl.
No.: |
16/303,570 |
Filed: |
May 23, 2017 |
PCT
Filed: |
May 23, 2017 |
PCT No.: |
PCT/CA2017/000133 |
371(c)(1),(2),(4) Date: |
November 20, 2018 |
PCT
Pub. No.: |
WO2017/197492 |
PCT
Pub. Date: |
November 23, 2017 |
Prior Publication Data
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|
|
Document
Identifier |
Publication Date |
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US 20200315528 A1 |
Oct 8, 2020 |
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Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
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62339419 |
May 20, 2016 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G16H
80/00 (20180101); A61B 5/0022 (20130101); G16H
50/30 (20180101); A61B 5/0205 (20130101); A61B
5/4848 (20130101); A61B 5/6801 (20130101); G16H
70/40 (20180101); G16H 50/70 (20180101); A61B
5/7465 (20130101) |
Current International
Class: |
A61B
5/00 (20060101); G16H 50/30 (20180101); A61B
5/0205 (20060101); G16H 80/00 (20180101); G16H
70/40 (20180101); G16H 50/70 (20180101) |
References Cited
[Referenced By]
U.S. Patent Documents
Primary Examiner: Kuo; Jonathan T
Attorney, Agent or Firm: Praxis
Parent Case Text
CROSS-REFERENCE TO RELATED APPLICATIONS
The present application claims priority on U.S. Provisional Patent
Application No. 62/339,419 filed on May 20, 2017 and incorporated
herein by reference in its entirety.
Claims
What is claimed is:
1. A computer implemented system for real-time, automatic and
prompted interactive monitoring of a target individual having a
health condition and having been assigned a therapeutic composition
at an assigned dosage thereof for treatment of the health
condition, the system providing for identifying the efficacy of
posology for the target individual with respect to administration
of the assigned therapeutic composition, the system providing for
determining whether the assigned dosage should be modified in order
to increase or decrease the dosage, the system comprising: a remote
controller comprising a processor and an associated memory of
processor executable code that when executed by the processor cause
the controller to execute computer implementable steps; a user
interface for a mobile handheld communications device, the user
interface being in communication with the remote controller via a
network and providing for receiving inputs from the user to be
communicated to the remote controller and to communicate outputs
from the remote controller to the user; a database in communication
with the remote controller and having stored thereon information
related to a plurality of health conditions, a plurality of
symptoms indicative of respective ones of the plurality of health
conditions, a plurality of therapeutic compositions for treating
respective ones of the plurality of health conditions, a plurality
of side effects associated to respective ones of the plurality of
the therapeutic compositions, and wherein the database further
comprises: a databank having stored thereon information related to
a plurality of previous target individuals, wherein the information
related to the plurality of previous target individuals comprises
the health conditions and related symptoms of the plurality of
previous target individuals, the therapeutic compositions assigned
the plurality of previous target individuals in treatment of the
health conditions of the plurality of previous target individuals,
and the side effects of therapeutic compositions assigned to the
plurality of previous target individual; a dose calculator
controller in communication with the database and comprising a
processor and an associated memory of processor executable code
that when executed by the processor causes the dose calculator
controller to execute computer implementable steps: receiving input
via a dose calculator interface in communication therewith of
clinical data regarding the posology of the plurality of
therapeutic compositions for treating the respective ones of the
plurality of health conditions; calculating clinical averages of
the input received via the dose calculator interface, wherein the
clinical averages consider the dose range and the temporal range of
administration of the therapeutic composition thereby providing a
dosage-temporal average; communicating the dosage-temporal average
to the database for storage thereon; wherein execution of the
processor executable code stored in the memory of the remote
controller causes the remote controller to execute the computer
implementable steps of: automatically storing on the database the
dosage-temporal average information communicated to the database;
automatically storing in the databank in real-time the information
related to the plurality of previous target individuals;
automatically statistically modifying the plurality of symptoms in
the database and the plurality of side effects in the database in
accordance with the information related to the plurality of the
previous target individuals; receiving information from the user
interface via user inputs regarding the target individual's health
condition and automatically comparing this information to the
database to match this information to at least one of the plurality
of health conditions in the database thereby automatically
providing a matched condition and identifying one or more of the
plurality of the statistically modified symptoms in the database
indicative of the matched condition thereby providing identified
symptoms; receiving information from the user interface via user
inputs regarding the therapeutic composition assigned to the target
individual, automatically comparing this information to the
database to match this information to at least one of the plurality
of therapeutic compositions in the database thereby automatically
providing a matched therapeutic composition, and automatically
identifying one or more of the plurality of the statistically
modified side effects in the database associated with the matched
therapeutic composition thereby automatically providing identified
side effects; automatically prompting the user via the user
interface by way of a visual and/or audial cues to provide
real-time user feedback regarding the possibility of the target
individual manifesting the identified symptoms or the identified
side effects within predetermined parameters stored within the
memory of the controller, wherein prompting within predetermined
parameters comprises: providing pre-determined questions stored in
the memory of the remote controller to the user in real-time via
the user interface related to the identified symptoms or the
identified side effects; schedule specific prompting at
predetermined times of the prompting based on the probable
occurrences of the identified symptoms or identified side effects
wherein the schedule specificity and the predetermined times based
on the probable occurrences of the identified symptoms or
identified side effects are stored in the memory of the remote
controller; prompting in real-time the individual to respond to the
presence or absence of identified symptoms and identified side
effects including the time of day thereof, the type thereof, the
particularity thereof, the severity thereof; continuously prompting
the user for feedback until the feedback is received; receiving the
feedback from the user in real-time by way of inputs via the user
interface; automatically determining in real-time based on the user
feedback whether the therapeutic composition assigned to the target
individual is administered at a dosage that should be modified by
automatically executing the following computer implementable steps:
i. automatically comparing the assigned dosage over a period of
predetermined time to the dosage-temporal average of the assigned
therapeutic composition, wherein the predetermined time is stored
within the memory of the controller; ii. automatically identifying
a discrepancy between the assigned dosage of the assigned
therapeutic composition over the period of predetermined time and
the dosage-temporal average for the assigned therapeutic
composition; iii. automatically and respectively comparing the user
feedback related to the manifested symptoms or manifested side
effects within the predetermined parameters with the identified
symptoms and identified side effects in order to respectively
identify symptom matches or side effect matches; iv. determining
that an assigned dosage should be increased based on an efficacy
score between (a) a presence of symptom matches, (b) an absence of
side effect matches and (c) the discrepancy in (ii), stored in the
memory of the controller; and v. determining that an assigned
dosage should be decreased based on an efficacy score between (a) a
presence of side effect matches, (b) an absence of symptom matches
and (c) the discrepancy in (ii), stored in the memory of the
controller; and automatically communicating in real-time the
determined modification of the assigned dosage via the user
interface.
2. A computer implemented system according to claim 1, wherein the
information related to the plurality of previous target individuals
further comprises identifiers associated with respective ones of
the plurality of target individuals thereby providing previous
identifiers.
3. A computer implemented system according to claim 2, wherein the
computer implemented steps further comprise: receiving information
from the user interface regarding the target individual's
identifiers and comparing this information to the previous
identifiers to assess similarities therebetween thereby providing
common identifiers; identifying the previous target individuals
with the common identifiers and with the matched condition and
matched therapeutic composition thereby providing common previous
target individuals; identifying in real-time the symptoms of the
common previous target individuals for the matched condition
thereby providing common symptoms and identifying the side effects
of the common previous target individuals for the matched
therapeutic composition thereby providing common side effects;
prompting and receiving real-time user feedback via the user
interface regarding the possibility of the target individual
manifesting the common symptoms or the common side effects;
determining in real-time based on the user feedback whether the
therapeutic composition assigned to the target individual is
administered at a dosage that should be modified in order to be
increased or decreased, wherein manifestation of common symptoms is
indicative of a dosage that should be increased and manifestation
of common side effects is indicative of a dosage that should be
decreased.
4. A computer implemented system according to claim 1, wherein the
database further comprises a plurality of predetermined posology
ranges related to the administration of respective ones of the
plurality of the therapeutic compositions for treating respective
ones of the plurality of health conditions, wherein the computer
implemented steps further comprise: receiving information via the
user interface regarding a prescribed posology for the target
individual and comparing this information to the plurality of
posology ranges for the matched therapeutic composition in treating
the matched health condition thereby identifying a predetermined
posology range for the target individual; comparing in real-time
the prescribed posology range with the predetermined posology range
to identify discrepancies therebetween; and determining in
real-time based on the user feedback and on the identified
discrepancies whether the prescribed posology range should be
modified to remove the identified discrepancies, wherein
manifestation of identified symptoms or identified side effects is
indicative of a prescribed posology range that should be
modified.
5. A computer implemented system according to claim 4, wherein the
database further comprises a databank of information related to a
plurality of previous target individuals, wherein the information
related to the plurality of previous target individuals comprises
plurality of previous posology ranges related to the administration
of respective ones of the plurality of the therapeutic compositions
for treating respective ones of the plurality of health
conditions.
6. A computer implemented system according to claim 5, wherein the
memory of computer implemented steps further comprises
statistically modifying the plurality of predetermined posology
ranges in the database in accordance with the information related
to the plurality of the previous target individuals.
7. A computer implemented system according claim 5, wherein the
information related to the plurality of previous target individuals
further comprises identifiers associated with respective ones of
the plurality of target individuals thereby providing previous
identifiers.
8. A computer implemented system according to claim 7, wherein the
computer implemented steps further comprise: receiving information
from the user interface regarding the target individual's
identifiers and comparing this information to the previous
identifiers to assess similarities therebetween thereby providing
common identifiers; identifying the previous target individuals
with the common identifiers and with the matched condition and
matched therapeutic composition thereby providing common previous
target individuals; processing the posology ranges of the common
previous target individuals to provide a statistically common
posology range; comparing the prescribed posology range with the
statistically common posology range to identify discrepancies
therebetween; and determining based on the user feedback and on the
identified discrepancies whether the prescribed posology range
should be modified to remove the identified discrepancies, wherein
manifestation of identified symptoms or identified side effects is
indicative of a prescribed posology range that should be
modified.
9. A computer implemented system according to claim 1, wherein the
computer implementable steps further comprise transmitting the
determined modification to the user interface.
10. A computer implemented system according to claim 1, wherein the
user interface is configured to be used by a user selected from the
group consisting of: the target individual, one or more physician,
one or more monitor and a combination thereof.
11. A computer implemented system according to claim 1, further
comprising one or more additional user interfaces, wherein the one
or more additional user interfaces are respectively configured to
display predetermined information regarding the target individual
as selectively programmed to be transmitted by the controller.
12. A computer implemented system according to claim 1, further
comprising biosensors mounted to the target individual and in
communication with the remote controller directly or via the user
interface for providing in real-time the controller with
information detected by the biosensors.
13. A computer implemented computer implemented system according to
claim 12, wherein the information detected by the biosensors
comprises: one or more symptoms, one or more side effects, one or
more identifiers and a combination thereof.
Description
TECHNICAL FIELD
The present disclosure relates to a system and method for
monitoring and identifying posology efficacy for an individual.
More particularly, but not exclusively, the present disclosure
relates to system end method for assisting a health professional or
researcher in identifying optimal posology for an individual and to
provide communication and coordination between actors in the
context of a medical or mental health condition.
BACKGROUND
The efficiency end side effects of medication are variable from
person to person, as are the symptoms of a given health issue.
There are currently limited methods for monitoring symptoms and
side-effects of medication and/or therapy in a longitudinal manner,
and no product on the market collects and analyses information
specifically for comparing several posologies or treatments in a
single individual.
Objects
An object of the present disclosure is to provide a system for
monitoring and identifying posology efficacy for an individual.
An object of the present disclosure is to provide a method for
monitoring and identifying posology efficacy for an individual.
SUMMARY
In accordance with an aspect of the present disclosure, there is
provided a system for monitoring and identifying the efficacy of
posology for a target individual having a health condition with
respect to administration of a therapeutic composition assigned to
the target individual for treatment of the health condition, the
system comprising: a user interface for being accessed by a user; a
database of: a plurality of health conditions, a plurality of
symptoms indicative of respective ones of the plurality of health
conditions, a plurality of therapeutic compositions for treating
respective ones of the plurality of health conditions, a plurality
of side effects associated to respective ones of the plurality of
the therapeutic compositions; and a controller in communication
with the user interface and with the database, the controller
comprising a memory of computer implementable steps for: receiving
information from the user interface regarding the target
individual's health condition and comparing this information to the
database to match this information to at least one of the plurality
of health conditions in the database thereby providing a matched
condition and identifying one or more of the plurality of symptoms
in the database indicative of the matched condition thereby
providing identified symptoms; receiving information from the user
interface regarding the therapeutic composition assigned to the
target individual, comparing this information to the database to
match this information to at least one of the plurality of
therapeutic compositions in the database thereby providing a
matched therapeutic composition, and Identifying one or more of the
plurality of side effects in the database associated with the
matched therapeutic composition thereby providing identified side
effects; prompting and receiving user feedback via the user
interface regarding the possibility of the target individual
manifesting the identified symptoms or the identified side effects;
determining based on the user feedback whether the therapeutic
composition assigned to the target individual is administered at a
dosage that should be modified in order to be increased or
decreased, wherein manifestation of identified symptoms is
indicative of a dosage that should be increased and manifestation
of identified side effects is indicative of a dosage that should be
decreased.
In accordance with an embodiment of the system, the database
further comprises a databank of information related to a plurality
of previous target individuals, wherein the information related to
the plurality of previous target individuals comprises the health
conditions and related symptoms of the plurality of previous target
individuals, the therapeutic compositions assigned the plurality of
previous target individuals in treatment of the health conditions
of the plurality of previous target individuals, and the side
effects of therapeutic compositions assigned to the plurality of
previous target individuate. In accordance with an embodiment of
the system, the memory of computer implemented steps further
comprises statistically modifying the plurality of symptoms in the
database end the plurality of side effects in the database in
accordance with the information related to the plurality of the
previous target individuals. In accordance with an embodiment of
the system, the information related to the plurality of previous
target individuals further comprises identifiers associated with
respective ones of the plurality of target individuals thereby
providing previous identifiers. In accordance with an embodiment of
the system, the memory of computer implemented steps further
comprises: receiving information from the user interface regarding
the target individual's Identifiers and comparing this information
to the previous identifiers to assess similarities therebetween
thereby providing common identifiers; identifying the previous
target individuals with the common identifiers and with the matched
condition and matched therapeutic composition thereby providing
common previous target individuals; identifying the symptoms of the
common previous target individuals for the matched condition
thereby providing common symptoms and identifying the side effects
of the common previous target individuals for the matched
therapeutic composition thereby providing common side effects;
prompting and receiving user feedback via the user interface
regarding the possibility of the target individual manifesting the
common symptoms or the common side effects; determining based on
the user feedback whether the therapeutic composition assigned to
the target individual is administered at a dosage that should be
modified in order to be increased or decreased, wherein
manifestation of common symptoms is indicative of a dosage that
should be increased and manifestation of common side effects is
indicative of a dosage that should be decreased.
In accordance with an embodiment of the system, the database
further comprises a plurality of predetermined posology ranges
related to the administration of respective ones of the plurality
of the therapeutic compositions for treating respective ones of the
plurality of health conditions, wherein the memory further
comprises the computer implemented steps of: receiving information
via the user interface regarding a prescribed posology for the
target individual and comparing this information to the plurality
of posology ranges for the matched therapeutic composition in
treating the matched health condition thereby identifying a
predetermined posology range for the target individual; comparing
the prescribed posology range with the predetermined posology range
to identify discrepancies therebetween; and determining based on
the user feedback and on the identified discrepancies whether the
prescribed posology range should be modified to remove the
identified discrepancies, wherein manifestation of identified
symptoms or identified side effects is indicative of a prescribed
posology range that should be modified. In accordance with an
embodiment of the system, the database further comprises a databank
of information related to a plurality of previous target
individuals, wherein the information related to the plurality of
previous target individuals comprises plurality of previous
posology ranges related to the administration of respective ones of
the plurality of the therapeutic compositions for treating
respective ones of the plurality of health conditions. In
accordance with an embodiment of the system, the memory of computer
implemented steps further comprises statistically modifying the
plurality of predetermined posology ranges in the database in
accordance with the information related to the plurality of the
previous target individuals. In accordance with an embodiment of
the system, the information related to the plurality of previous
target individuals further comprises identifiers associated with
respective ones of the plurality of target individuals thereby
providing previous identifiers. In accordance with an embodiment of
the system, the memory of computer implemented steps further
comprises: receiving information from the user interface regarding
the target individual's identifiers and comparing this information
to the previous identifiers to assess similarities therebetween
thereby providing common identifiers; identifying the previous
target individuals with the common identifiers and with the matched
condition and matched therapeutic composition thereby providing
common previous target individuals; processing the posology ranges
of the common previous target individuals to provide a
statistically common posology range; comparing the prescribed
posology range with the statistically common posology range to
identify discrepancies therebetween; and determining based on the
user feedback and on the identified discrepancies whether the
prescribed posology range should be modified to remove the
identified discrepancies, wherein manifestation of identified
symptoms or identified side effects is indicative of a prescribed
posology range that should be modified.
In accordance with an embodiment of the system, the memory of
computer implementable steps further comprises transmitting the
determined modification to the user interface.
In accordance with an embodiment of the system, the user interface
is configured to be used by a user selected from the group
consisting of: the target individual, one or more physician, one or
more monitor and a combination thereof.
In accordance with an embodiment, the system further comprises one
or more additional user interfaces, wherein the one or more
additional user interfaces are respectively configured to display
predetermined information regarding the target individual as
selectively programmed to be transmitted by the controller.
In accordance with an embodiment, the system further comprises
biosensors mounted to the target individual and in communication
with the controller directly or vie the user interface for
providing the controller with information detected by the
biosensors. In accordance with an embodiment of the system, the
information detected by the biosensors comprises: one or more
symptoms, one or more side effects, one or more identifiers and a
combination thereof.
In accordance with an aspect of the present disclosure, there is
provided a method for monitoring and identifying the efficacy of
posology for a target individual having a health condition with
respect to administration of a therapeutic composition assigned to
the target individual for treatment of the health condition, the
method comprising: providing a database of: a plurality of health
conditions, a plurality of symptoms indicative of respective ones
of the plurality of health conditions, a plurality of therapeutic
compositions for treating respective ones of the plurality of
health conditions, a plurality of side effects associated to
respective ones of the plurality of the therapeutic compositions;
receiving information regarding the target individual's health
condition; automatically comparing this information in real-time to
the database to match this information to at least one of the
plurality of health conditions in the database thereby
automatically providing in real-time a matched condition;
automatically identifying in real-time one or more of the plurality
of symptoms in the database indicative of the matched condition
thereby providing identified symptoms; receiving information from
the user interface regarding the therapeutic composition assigned
to the target individual; automatically comparing this information
in real-time to the database to match this information to at least
one of the plurality of therapeutic compositions in the database
thereby providing a matched therapeutic composition; automatically
Identifying in real-time one or more of the plurality of side
effects in the database associated with the matched therapeutic
composition thereby providing identified side effects; prompting
and receiving user feedback regarding the possibility of the target
individual manifesting the identified symptoms or the identified
side effects; automatically determining in real-time based on the
user feedback whether the therapeutic composition assigned to the
target individual is administered at a dosage that should be
modified in order to be increased or decreased, wherein
manifestation of identified symptoms is indicative of a dosage that
should be increased and manifestation of identified side effects is
indicative of a dosage that should be decreased.
In accordance with an embodiment of the method, the database
further comprises a databank of information related to a plurality
of previous target individuals, wherein the information related to
the plurality of previous target individuals comprises the health
conditions and related symptoms of the plurality of previous target
individuals, the therapeutic compositions assigned the plurality of
previous target individuals in treatment of the health conditions
of the plurality of previous target individuals, and the side
effects of therapeutic compositions assigned to the plurality of
previous target individuals. In accordance with an embodiment, the
method further comprises: automatically statistically modifying the
plurality of symptoms in the database and the plurality of side
effects in the database in accordance with the information related
to the plurality of the previous target individuals. In accordance
with an embodiment of the method, the information related to the
plurality of previous target individuals further comprises
identifiers associated with respective ones of the plurality of
target individuals thereby providing previous identifiers. In
accordance with an embodiment, the method further comprises:
receiving information regarding the target individual's
identifiers; automatically comparing in real-time the information
regarding the target individual's identifiers to the previous
identifiers to assess similarities therebetween thereby providing
common identifiers; automatically identifying in real-time the
previous target individuals with the common identifiers and with
the matched condition and matched therapeutic composition thereby
providing common previous target individuals; automatically
identifying in real-time the symptoms of the common previous target
individuals for the matched condition thereby providing common
symptoms; automatically identifying in real-time the side effects
of the common previous target individuals for the matched
therapeutic composition thereby providing common side effects;
prompting and receiving user feedback regarding the possibility of
the target Individual manifesting the common symptoms or the common
side effects; automatically determining in real-time based on the
user feedback whether the therapeutic composition assigned to the
target individual is administered at a dosage that should be
modified in order to be increased or decreased, wherein
manifestation of common symptoms is indicative of a dosage that
should be increased and manifestation of common side effects is
indicative of a dosage that should be decreased.
In accordance with an embodiment of the method, the database
further comprises a plurality of predetermined posology ranges
related to the administration of respective ones of the plurality
of the therapeutic compositions for treating respective ones of the
plurality of health conditions, the method further comprising:
receiving Information regarding a prescribed posology for the
target individual; automatically comparing in real-time this
information to the plurality of posology ranges for the matched
therapeutic composition in treating the matched health condition
thereby identifying a predetermined posology range for the target
individual; automatically comparing in real-time the prescribed
posology range with the predetermined posology range to identify
discrepancies therebetween; and automatically determining in
real-time based on the user feedback and on the identified
discrepancies whether the prescribed posology range should be
modified to remove the identified discrepancies, wherein
manifestation of identified symptoms or identified side effects is
indicative of a prescribed posology range that should be modified.
In accordance with an embodiment of the method, the database
further comprises a databank of information related to a plurality
of previous target individuals, wherein the information related to
the plurality of previous target individuals comprises plurality of
previous posology ranges related to the administration of
respective ones of the plurality of the therapeutic compositions
for treating respective ones of the plurality of health conditions.
In accordance with an embodiment, the method further comprises
statistically modifying the plurality of predetermined posology
ranges in the database in accordance with the information related
to the plurality of the previous target individuals. In accordance
with an embodiment of the method, the information related to the
plurality of previous target individuals further comprises
identifiers associated with respective ones of the plurality of
target individuals thereby providing previous identifiers. In
accordance with an embodiment, the method further comprises:
receiving information regarding the target individual's
identifiers; automatically comparing in real-time the information
regarding the target individual's identifiers to the previous
identifiers to assess similarities therebetween thereby providing
common identifiers; automatically identifying in real-time the
previous target individuals with the common identifiers and with
the matched condition and matched therapeutic composition thereby
providing common previous target individuals; automatically
processing in real-time the posology ranges of the common previous
target individuals to provide a statistically common posology
range; automatically comparing in real-time the prescribed posology
range with the statistically common posology range to identify
discrepancies therebetween; and automatically determining in
real-time based on the user feedback and on the identified
discrepancies whether the prescribed posology range should be
modified to remove the identified discrepancies, wherein
manifestation of identified symptoms or identified side effects is
indicative of a prescribed posology range that should be
modified.
In an embodiment, the method further comprises automatically
transmitting in real time the determined modification to a user. In
an embodiment of the method, the user is selected from the group
consisting of: the target individual, one or more physician, one or
more monitor and a combination thereof.
In an embodiment, the method further comprises automatically
transmitting in real time to one or a plurality of selectively
predetermined information regarding the target individual.
In an embodiment, the method further comprises: mounting biosensors
the target individual; and receiving in real-time information
detected by the biosensors. In an embodiment of the method, the
information detected by the biosensors comprises: one or more
symptoms, one or more side effects, one or more identifiers and a
combination thereof.
Other objects, advantages and features of the present disclosure
will become more apparent upon reading of the following
non-restrictive description of illustrative embodiments thereof,
given by way of example only with reference to the accompanying
drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
In the appended drawings:
FIG. 1 is a schematic representation of a system for monitoring and
identifying posology efficacy for an individual in accordance with
a non-restrictive Illustrative embodiment of the present
disclosure;
FIG. 2 is a schematic representation of the communication between
the controller and both the user interface and the database of the
system of FIG. 1, in accordance with a non-restrictive illustrative
embodiment of the present disclosure;
FIG. 3 is a schematic representation of user interaction with a
system for monitoring and identifying posology efficacy for an
individual in accordance with a non-restrictive illustrative
embodiment of the present disclosure;
FIG. 4 is a schematic representation of how posology variables and
the posology intervals comprise the final randomised titration
schedule provided by the system and/or method for identifying
posology efficacy for an individual in accordance with a
non-restrictive illustrative embodiment of the present
disclosure;
FIG. 5 is a schematic representation of a titration system provided
by the system and/or method for identifying posology efficacy for
an individual in accordance with a non-restrictive illustrative
embodiment of the present disclosure;
FIG. 6 is a schematic representation of communication relationships
between users, accounts and dossiers of the system and/or method
for identifying posology efficacy for an individual in accordance
with a non-restrictive illustrative embodiment of the present
disclosure;
FIG. 7 is a schematic representation of user interactions in the
communication system of the system for monitoring and identifying
posology efficacy for an individual in accordance with a
non-restrictive illustrative embodiment of the present
disclosure;
FIG. 8 is a schematic representation of user interactions in the
communication system of the system for monitoring and identifying
posology efficacy for an individual in accordance with a
non-restrictive illustrative embodiment of the present
disclosure;
FIG. 9 is a schematic representation of the use of data provided by
the system for monitoring and identifying posology efficacy for an
individual to predict treatment outcomes in accordance with a
non-restrictive illustrative embodiment of the present disclosure;
and
FIG. 10 is an illustration of a user interface of the system for
monitoring and identifying posology efficacy for an individual in
accordance with a non-restrictive Illustrative embodiment of the
present disclosure.
DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS
Generally stated and in accordance with an aspect of the present
disclosure, there is provided a system for monitoring and
identifying the efficacy of posology for a target individual having
a health condition with respect to administration of a therapeutic
composition assigned to the target individual for treatment of the
health condition. The system comprises a user interface for being
accessed by a user, a database and a controller in communication
with the user interface and the database. The database includes
pluralities of health conditions, of therapeutic compositions for
treating these health conditions, and of side effects associated to
these therapeutic compositions. The controller comprises a memory
of computer implementable steps. The controller receives
information from the user interface regarding the target
Individual's health condition and the therapeutic composition
assigned to the target individual. The received information is
compared to the database provide a match with at least one of the
plurality of health conditions in the database thereby providing a
matched condition and to provide a match with at least one of the
plurality of therapeutic compositions in the database thereby
providing a matched therapeutic composition. The symptoms of the
matched condition are identified providing identified symptoms. The
side effects of the matched therapeutic compositions are identified
thereby providing identified side effects. The controller prompts
and receives user feedback regarding the possibility of the target
individual manifesting the identified symptoms or the identified
side effects. Based on the user feedback, the controller determines
whether the therapeutic composition assigned to the target
individual is administered at a dosage that should be modified in
order to be increased or decreased. Manifestation of identified
symptoms is indicative of a dosage that should be increased and
manifestation of identified side effects is indicative of a dosage
that should be decreased.
Generally stated and in accordance with an aspect of the present
disclosure, there is provided a method for monitoring and
identifying the efficacy of posology for a target individual having
a health condition with respect to administration of a therapeutic
composition assigned to the target individual for treatment of the
health condition. The method comprises: providing a database
pluralities of health conditions, of therapeutic compositions for
treating these health conditions, and of side effects associated to
these therapeutic compositions; receiving information regarding the
target individual's health condition; automatically comparing this
information in real-time to the database to match this information
to at least one of the plurality of health conditions in the
database thereby automatically providing in real-time a matched
condition; automatically identifying in real-time one or more of
the plurality of symptoms in the database indicative of the matched
condition thereby providing identified symptoms; receiving
information from the user interface regarding the therapeutic
composition assigned to the target individual; automatically
comparing this information in real-time to the database to match
this information to at least one of the plurality of therapeutic
compositions in the database thereby providing a matched
therapeutic composition; automatically identifying in real-time one
or more of the plurality of side effects in the database associated
with the matched therapeutic composition thereby providing
identified side effects; prompting and receiving user feedback
regarding the possibility of the target individual manifesting the
identified symptoms or the identified side effects; automatically
determining in real time based on the user feedback whether the
therapeutic composition assigned to the target individual is
administered at a dosage that should be modified in order to be
increased or decreased, wherein manifestation of identified
symptoms is indicative of a dosage that should be increased and
manifestation of identified side effects is indicative of a dosage
that should be decreased.
The system prompts the user (including other actors, such as
parents or spouses) to collect symptom and side-effect data in a
systematic and responsive manner, and displays the collected data
in a number of formats specifically designed to assist users,
health professionals and other actors to make health and
treatment-related decisions. Several prescriptions or treatments
can be compared in a single user during a titration period to
determine optimal posology or treatment; the logistics (e.g.,
scheduling and interaction with the participating pharmacy) is
handled by the application that also provides a method of
systematically collecting and reporting longitudinal medication
efficiency and side effects. The application also provides a
communication platform so that data collection can be done in a
systematic, interactive and coordinated manner across a large
number of actors.
The present disclosure helps minimize the undesirable effects of a
medical or mental health condition, including drug withdrawal. It
achieves this via two interrelated but standalone systems: 1) a
system assisting a health professional or researcher in identifying
optimal posology for an individual (posology system) and 2) a
system providing communication and coordination between a person
with a medical or mental health condition and individuals involved
with its treatment (communication system). In an embodiment, the
application is intended for interface devices such as smartphones
mobiles, tablets, personal computers and the like.
In an embodiment, the present posology system is used to establish
optimal posology for medication that an individual is currently
taking or will be taking. Optimal posology is defined as the most
efficient timing (when to take the medication), type (what type of
medication to take, if several medications are available for a
particular condition, and suitable for a particular individual) and
the amount (typically in mg) of medication to achieve the most
benefits from the medication while avoiding the most of its
disadvantages (e.g., side effects), in an embodiment, this is done
by prompting and allowing for systematic evaluations of the
medication's benefits and disadvantages, and by subsequently
analysing and displaying this feedback to assist a prescribing
physician in adjusting or initiating a prescription. The disclosure
also uses a system (i.e., titration system) to examine the effects
of several different posologies, so that they may be compared in a
systematic and objective manner akin to a double blind clinical
study (albeit with a single participant).
In an embodiment, several individuals (users) use the present
system and method in order to establish en optimal posology for a
single individual (target individual). Users can be, but are not
limited to, relatives, teachers, social workers, psychologists,
lawyers, and health professionals. These individuals use the
application to communicate data that will be used to establish
optimal posology for the target individual. In an embodiment, the
application requires at least a target individual and a prescribing
physician (or researcher). The target individual may also be a
user. In addition to being used in a physician-patient setting, the
application can be used for research uses (e.g., determining the
effects of drugs under development).
Each user has a unique instance of the application on their user
device (e.g., mobile, desktop computer, etc.); the different users
are linked together via an internet connection and a centralized
server. The users' applications are linked insofar as they are used
with regards to a single medicated-individual. The interface of the
application is dependent upon the relationship between the user and
the medicated-Individual. For example, the medicated-individual's
parent has a parent account that displays information and
functionalities that is relevant to parents, while the
medicated-individual's physician has a physician account that
provides information and functionalities pertinent to a health care
professional.
Turning now to FIGS. 1 and 2, a non-restrictive illustrative
embodiment of the present system and method will be discussed.
FIG. 1 shows the system 10 for identifying optimal posology of an
Individual 11 having a condition such as a medical or mental health
condition. The system 10 includes a user interface 12 for being
accessed by the individual 11 as will be explained hereinbelow. The
user interface 12 is in communication with a remote controller 14
such as a server for example. The controller 14 includes a memory
16 of computer implementable steps and is in communication with a
database 18.
The database 18 comprises information related to a plurality of
conditions 18-C including the symptoms 18-S of these conditions
18-C, the therapeutic compositions 18-D related to the treatment of
these conditions 18-C as well as the side effects 18-E of these
therapeutic compositions 18-D.
In an embodiment, the database 18 comprises information related to
an average posology 18-P for a given therapeutic composition 18-D.
This average posology 18-P is based on clinical data averages known
in the art.
In another embodiment, the database 18 is in communication with a
dose calculator 20 that provides information thereto regarding the
posology 20-P of the therapeutic compositions 18-D for treating
conditions 18-C. The posology 20-P provided by the dose calculator
20 is based on clinical data averages and provides a dosage range
and temporal schedule for a given therapeutic composition 18-D to
treat a given condition 18-C of an individual based on the
individual's profile as compared to the clinical data averages.
Examples of dose calculators include without limitation, the system
and methodology published by Guillame Bonnefois, Developpement
d'algorithmes d'individualisation TDAH et don implementation en une
application interactive, Universite de Montreal-Faculte de
Pharmacia, 6 Dec. 2013, which is incorporated herein in its
entirety.
In an embodiment, the posology 20-P is communicated to the database
18 directly or via the controller 14. In an embodiment, the average
posology 18-P is readjusted in accordance with 20-P. In an
embodiment, the average posology 18-P is replaced by 20-P end thus
the dose calculator 20 is the database 18. In one embodiment, the
database provided herein is a combined unit of database 18 and dose
calculator 20. In one embodiment, the database herein is a hybrid
unit of the database 18 and dose calculator 20.
In one embodiment, the clinical averages provided by the database
18 and/or dose calculator 20 also considers the temporal range of
administration of the therapeutic composition and not only the dose
range. Therefore, posology averages are a function of dose of
administration and time of administration. In one example, this
dosage-temporal average is assigned an efficacy score based on
absence of symptoms and absence of side effects. In an embodiment,
this efficacy score is also a function of other identifiers of
groups or categories of individuals such as age, location, height,
weight, location, activity, habits, general health including having
other medical or mental health conditions, consumption of other
therapeutic compositions end even sociological and psychological
factors.
In one example, the individual 11 inputs data 11-F via the user
interface 12 regarding their condition (as determined by their
physician), the therapeutic composition(s) they are consuming as or
the treatment provided by their physician, and other required
identifier or classification information such as age, gender,
weight and the like which is required by the dose calculator 20 to
provide a range based on the clinical data averages for a given
group classification of individuals, for example, one group Z may
consist of: individuals suffering from condition X consisting of
males between the ages of 35-45, having a Body Mass Index of
between 25-30, and a sedentary lifestyle. In one example, condition
X is treated by composition Y, the dose calculator 20 contains
clinical averages data regarding the efficacy score of Y based on
both dose and time of administration for group X. If the individual
fits the profile identifiers of group Z, then the controller 14
retrieves this information from the dose calculator 20 and the
initial posology 20-P is retrieved.
The initial posology 18-P or 20-P provided by the system 10, is
often quite long and burdensome for the physician to sift through
it via a long titration process in order to uncover the optimal
dosage for the individual 11.
The system 10 provides for further narrowing down the initial
posology 18-P or 20-P in order to identify the optimal posology for
the individual 11.
The database 18 includes a data bank 18-B of users populated by
various individuals 111 who have already uncovered their optimal
posology via the computer implementable steps provided herein and
the subsequent titration process explained further below. In this
way, the controller 14 compares the information 11-F received by
the individual as will be further explained below with the
information 111-F received by the plurality of individuals and
stored in the data bank 18-B. It should be noted that the data bank
18-B is updated in real time by a plurality of users 111 of the
system 10. Therefore, the data bank 18-B is being modified in real
time by the plurality of user feedback 111-F as will be further
exemplified below.
As such, an individual's feedback 11-F (including identifiers such
es profile) will be compared to the accumulated feedback 111-F
(including identifiers such as profiles) of the data bank 18-B
having the same condition 18-C as the individual 11 and using the
same therapeutic composition 18-D as the individual 11.
Accordingly, the controller 14 will identifies a sub-range within
the initial range 18-P or 20-P, based on the similarity of the
individual's profile 11-F with the accumulated profiles 111-F of
the data bank 18-B.
The individual's profile 11-F is based not only on identifiers
(11-I, see FIG. 2) such as age, gender, location, height, weight,
habits, general health and the like but on feedback assessed in a
longitudinal manner and accumulated by the controller 14. The
individual's feedback 11-F is prompted 14-Q by the controller 14
via the user interface 12, over several days (one or more times a
day) which requests symptom specific (to the individual's
condition) and side effect specific (to the therapeutic
composition) information from the individual 11. In posology, there
are two general thresholds: if the dose is too low, the individual
will experience symptoms of the condition and if the dose it too
high the individual will experience side effects of the therapeutic
composition used to treat the condition. Thus, the goal is to find
the optimal dosage having the greatest efficacy while substantially
avoiding the side effects between these two thresholds. Moreover,
other factors may influence the efficacy of the therapeutic
composition such as the time of day of administration and other
general health conditions. The efficacy is also a function of a
variety of other factors related to the individual 11 (age, weight,
height, general health, consumption of other therapeutic
compositions etc,), Therefore, the individual's dosage, symptoms,
side effects, identifiers (11-F/11-I) will be compared to the
accumulated profiles, generally denoted as 111-F (including 111-I,
see FIG. 2). More specifically, the longitudinally assessed
individual's profile 11-F is compared in real time to the
accumulated profiles 111-F which Includes feedback assessed in a
similar fashion as that of the individual 11. The goal of the
comparison between 11-F and 111-P is to identify a smaller dosage
sub-range of 16-P (or 20-P as discussed above). If the sub-range is
determined to be too large by the controller 14 based on
predetermined parameters (such as providing a less cumbersome
titration process), the controller 14 continues to request further
symptom specific and side effect specific information (14-Q) on the
basis of the foregoing comparison (13) to further narrow down the
dosage range until a resulting dosage range is provided that meets
the pre-determined parameters of the controller 14. In an
embodiment, these predetermined parameters comprise a titration
program of a preferred maximum set of days that provides for
determining the optimal posology as will be further discussed
below.
In one embodiment, the controller 14 implements the step of
Identifying the highest efficacy score (as discussed above) for an
individual 11 based on the closest similarity between 11-F and
111-F.
In an embodiment, once the above iteration process is complete (the
Iteration process comprising several rounds of prompting 14-Q,
feedback 11-F and comparison 13), the resulting dosage range is
reported to the physician of the individual 11 via a physician
interface 22 and a titration program is set up within the resulting
dosage range. In one example, the physician provides a kit to the
individual with a dosage protocol to be followed for several days
to identify the optimal posology for the Individual within the
provided resulting dosage range.
The physician informs the controller 14 of the titration program.
During the titration program, the controller 14 effectuates a
second iteration process by continuing to monitor the individual 11
by prompting (14-Q) the individual 11 to respond (11-F) to symptom
specific and side effect specific questions in order to identify
the optimal posology within the dosage range of the titration
program and report same to the physician thereby setting the
optimal dosage.
With reference to both FIGS. 1 and 2, the controller 14 receives
via the interface 12 the individual's basic information (11-F)
including their condition, (11-C) the therapeutic composition
(11-D) and dosage (11-P) thereof that they have been prescribed as
well as other identifiers (11-I) (e.g. gender, age, weight, height,
BMI, activity, general health questions etc).
The controller 14 implements a series of iteration steps based on
the information 11-F it has received from the individual 11 in
comparison 13 to the information of the database 18.
Accordingly, the controller 14 compares 13 an individual's
condition 11-C, the therapeutic composition 11-D prescribed to the
individual (11), the dosage 11-P (which can include the time of
dosage administration) and the other identifiers 11-I to the data
of the database 18. Namely, the database 18 comprises predetermined
knowledge of the symptoms 18-s of the condition 18-C and the side
effects 18-E of the therapeutic composition 18-D as well as a
clinical average of the posology 18-P therefor. The controller 14
thus makes an initial comparison 13 of 11-C to 18-C, of 11-S to
18-S, of 11-D to 18-D, of 11-E to 18-E, of 11-P to 18-P.
The data of the database 18 is modulated by machine learning. A
plurality of individuals 111 (see FIG. 1) have provided and
continue to provide feedback 111-F along with their identifiers
111-F. The controller seeks to identify similarities between 11-F
and 111-F and similarities between 11-I and 111-F for a common
therapeutic composition (11-D.revreaction.40 18-D) treating a
common condition (11-C.revreaction.18-C) in order to identify the
optimal posology range in which the individual 11-F confirms an
absence of symptoms (11-S) and side effects (11-E).
In an embodiment, as shown in FIG. 1, the system 10 includes the
physician interface 22 as mentioned above as well as a monitor
interface 24 in communication with the controller 14. The monitor
can be an additional health care professional, a care giver, a
parent, or any other type of supervisor and/or monitor as can be
contemplated within the context of the present disclosure. As such,
the monitor can receive the real-time feedback inputs of the
individual user and/or the prompted questions from the controller
14 to the individual 11. When the individual is a child, the
monitor ca be a parent and batter assess the prompted questions
14-Q of the controller 14 for providing more articulate input 11-F
by the monitor to the controller 14. Moreover, the monitor can
receive reports based on the individual's feedback (11-F)
similarity to the aggregate feedback 111-F. In another embodiment,
the physician enters the individual's general initial identifies
via the physician interface 22. In another embodiment, the
individual's monitor provides the initial identifier via the
monitor interface 24.
The memory 18 comprises a plurality of computer-Implemented
processes for the above iterations based on known statistical
algorithms, computational statistics, machine learning and
algorithms therefor, pattern recognition, bioinformatics,
biostatistics, data mining, iterative methods in statistical
estimation, clustering or cluster analysis and the like as is known
in the art.
In an embodiment, the memory 16 uses the foregoing algorithms in
Implementing the protocol generally exemplified below.
Computerl Implemented Protocol Chart of System 10 and Components
Thereof
System (10) Comprises: A controller (14) A user interface (12) for
the individual (11) to provide data input (11-F including 11-I) A
physician interface (22) A monitor interface (24) A database 18
comprising a user updated databank for receiving information from a
plurality of users (111) including individuals (11), physicians and
monitors, thereby comprising a plurality of feedback 111-F
including an aggregate of identifiers 111-I from the plurality of
users 111 A dose calculator (20) that can form part of the database
(18) or be one in the same with database (18)
Database (18) Comprises: List of medical or mental health
conditions (18-C) List of therapeutic compositions (18-D) for
treating respective conditions (18-C) List of symptoms (18-S)
including incremental degrees thereof related to a respective
condition (18-C) List of side effects (18-E) including incremental
degrees thereof related to therapeutic compositions (18-D) The
known clinical average posology (18-P) for a therapeutic
composition (18-D) in treating a respective condition (18-C)
Data Input (11-F) Individual's medical or mental health condition
(11-C) Therapeutic composition (11-D) to treat the condition (11-C)
Prescribed posology (11-P) Individual's identifiers (11-I)
including real time health conditions independent of 11-C
Comparison (13) Between Individual Data Input (11-F/11-I) and
Database Knowledge Controller (14) compares (13) data input to
information of database (18), including: Matching 11-C to the
corresponding 18-C (denoted herein as M1) Matching 11-D to the
corresponding 18-D (denoted herein as M2) Identifying symptoms
related to 11-C=18-S related to 18-C (denoted herein as M1-S)
identifying side effects related to 11-D=18-E related to 18-D
(denoted herein as M2-E) Comparing (13) 11-P to 1B-P for the same
therapeutic composition (11-D=18-D) in the treatment of the same
condition (11-C=18-C) and identifying discrepancies
therebetween.
User Feedback (11-F) The controller (14) prompts the individual
(11) to provide feedback (11-F) with symptom specific inquiries
(14-Q) in view of the identified symptoms (M1-S) and with side
effect specific inquiries (14-Q) in view of the identified side
effects (M2-E). Prompting (14-Q) is schedule-specific i.e. it
occurs at predetermined times based on M1, M2, M1-S, M2-2 11-F
(Including 11-I) Controller (14) prompts (14-Q) the individual (11)
to respond to: the presence or absence of identified symptoms
(M1-S) or identified side effects (M2-E) the time of day that M1-S
or M2-E occurred the type or particularities of M1-S or M2-E the
severity of M1-S or M2-E
User-Updated Databank a) A real-time updated bank (18-B) of
feedback (111-F including 111-I) from a plurality of users (111)
regarding 18-S, 18-E and 18-P b) A real-time updated bank (18-B) of
users (111) having respective identifiers (111-I)
Modulation of Data--Machine Learning The controller (14) clusters
the feedback (111-F) of the User-Updated Databank (18-B) based on
predetermined commonalities and updates the Database (18) thereby
readjusting in real-time 18-S related to a particular condition
18-C, as well side effect 18-E and posology 18-P related to a
particular therapeutic composition 18-D for treating that
particular condition 18-C The controller (14) compares (13) the
individual's data input/feedback (11-F) to the readjusted
information of the Database (18) by way of the foregoing Comparison
Between individual Data input and Database Knowledge The controller
(14) clusters users (111) in the User-Updated Databank (18-B)
having common identifiers (111-I) based on the identifiers (11-I)
of the individual (11), thereby matching the individual (11) with
one or more clustered categories of users 111 based on
commonalities between 11-I and 111-I The controller (14) identifies
(13) the clusters of common feedback (111-F) related one or more
clustered identifier categories (111-I) matching the individual's
identifiers (11-I) The controller (14) compares (13) the feedback
(11-F) of the individual (11) obtained in a linear manner to the
clusters of common feedback (111-F) related to one or more
clustered categories (111-F) The controller (14) determines (13)
the greatest similarity of the individual's feedback (11-F)
obtained over several iterations to the clusters of common feedback
(111-F) related to one or more clustered categories (111-I) having
the greatest similarity to the individual's identifiers (11-I)
Iterations The controller (14) seeks to narrow the posology range
18-P based on at least two parameters: to the smallest range
containing the probable optimal posology for a given individual;
the above range being of a length that provides for a titration
program that does not exceed a predetermined preferred number of
days The controller (14) implements several iterations of data
input (11-F) from the Individual (11) and of comparisons (13)
thereof to the modulated or adjusted data of the database (18),
returning with new inquiries (14-Q) for the individual (11) for
still further data input (11-F) for still further comparisons (13)
until a satisfactory posology range is obtained based on the at
least two parameters
Results Based on the iteration above, the controller (14) provides
a posology range on the basis of an individual's similarity to the
aforementioned clusters and the predetermined parameters The
individual's posology 11-P is readjusted and a new suggested
posology 11-P' is provided comprising a probable optimal
posology
Titration Program Based on the above RESULTS, the physician sets up
a titration program (11-T) for the individual The physician
communicates the titration program to the Controller (14)
Titration Program Monitoring--Further iteration The controller (14)
monitors the titration program (11-T) A The controller prompts
(14-Q) user feedback (11-F) during the titration program (11-T) A
The controller 14 implements further iterations including inquiries
(14-Q) and comparisons (13) as described above in order to identify
(13) the optimal posology within 11-P'
Reports and Databank Update The controller (14) provides a report
to the physician via the physician interface (22) The physician
confirms the optimal posology based on the result, iteration and
comparison reports The physician communicates their confirmation to
the controller (14) The controller (14) updates the databank (18-B)
in real time regarding the individual's identifiers (11-I), the
individual's feedback (11-F), the suggested posology (11-P'), the
titration program (11-T), the titration program related user
feedback (11-F), the computer obtained optimal posology within
11-P', the physician-confirmed optimal posology
The present system therefore provides of monitoring and identifying
posology efficacy. Moreover, the present system provides for
monitoring and identifying side effect severity. Furthermore, the
prese system provides for monitoring and identifying symptom
severity.
General Description of Non-Limiting Practical Example (System
100)
System 100 comprises an application (i.e. application software for
the computer implementable steps, also known as app) that visually
communicates with the users via the interface.
As demonstrated by FIG. 3, the application of system 100 prompts
(arrows A) three users to make an evaluation via respective user
interfaces 12 (User A Device, User B Device and User C Device) with
regards to the medicated-individual Albert. The users in this case
are Albert's Mother who communicates with the server (comprising
both a controller 14 and a database 18) via User A Device, Albert's
teacher who communicates with the server via User B Device and
Albert's prescribing physician who communicates with the server via
User C Device. These three users evaluate Albert in accordance with
the prompted questions (arrows A) and provide their user-feedback
(arrows B) back into the server. The system 100 analyses the
accumulated user-feedback from all users in order to adjust the
evaluation deployment schedules of the users to better measure the
strengths and weaknesses of the current posology (C). When the
prescribing physician makes the request, the server, analyses the
accumulated user-feedback from all users (D) in order to generate a
physician report to assist the physician in adjusting the posology
for optimal effect (E), A new prescription is made (F), and users
are prompted make evaluations with regard to this new posology (A).
The same data that was used in the physician's report can be
inputted in third party software (G). Data taken from a biosensor
device (heart rate and movement data) is Inputted via the parent's
device into the server database (H).
Evaluations
In an embodiment, the system 100 prompts the users to make
evaluations concerning the medicated-individual. The nature of the
evaluations and their deployment schedules are specific to the
account type (e.g., parent and teachers have different evaluation
objectives and schedules). The evaluations are all preformed on the
user-device (interface), and consist of questionnaires,
computerised tests (e.g., neurocognitive evaluations) any other
means of collecting data that pertains to the efficiency or
negative effects of the medication, and to the state of the
condition for which medication is being prescribed.
Of course, as discussed for system 10, it is possible for the
target individual make self-evaluations.
Evaluation Prompts
The application of the system 100 sends reminders (email and
in-device prompts such as push-notification and pop-ups) to
initialize or complete specific evaluations that are past due
date.
Biosensor Feedback
Measures such as heart rate, respiration and skin conductance,
derived from wearable technology and other sensor-based
technologies, send data into the target individual's device that is
in turn sent to the server for analysis. For example, heart rate
and movement data from a wearable biosensor watch provides
pertinent data concerning the side effects and efficiency of ADHD
medication.
Analysis of Data
Data collected from these evaluations (i.e., user-feedback) are
sent to the sewer. The data is analyzed along with the data from
other users concerning the same target individual; these analysis
may modify the evaluation schedules of the users (e.g., reports of
insomnia in a symptom questionnaire for ADHD medication will prompt
a daily sleeping evaluation questionnaire for parent accounts, and
will add a sleepiness scale on the evaluations of teacher
accounts).
Physician Report
When prompted by the prescribing physician, the collected data is
analyzed and presented in a report form on the prescribing
physician's user-device. The application selects, synthesizes,
summarizes and produces statistical analyses with the data
collected from user-feedback; this information is presented in a
concise manner by means of tables, and graphical representations
such as bar graphs, line graphs and pie charts, to assist the
prescribing physician in providing an optimal posology to the
medicated user. The report content and structure is customizable by
the physician.
Titration System
The titration system in accordance with a non-limiting example
provided herein allows to test the effects of several different
posologies on an individual. Individual differences that are
difficult or expensive to predict (e.g., variation in brain
architecture) cannot be accounted for when prescribing a drug. The
titration system addresses this issue by allowing users and a
prescribing doctor to examine the effects of several different
posologies during a period of several weeks. Because neither the
parents nor the doctor knows which of the four weeks (or examples,
(of course any number of weeks can be provided) are associated with
the four different posologies, for example, (i.e., double blind
procedure) (of course any number of posologies can be provided) an
objective evaluation of the best posology is possible. The system
100 allows for pertinent data (feedback) to be collected by the
target user (and other users) and strategically displayed to better
evaluate which of the posologies are best for them.
The objective of the titration system to examine the effects of
several different posologies, on a target individual, so that they
may be compared in a systematic end objective manner. The titration
system uses the features described above to prompt evaluations,
generate user-feedback data and physician reports in order to
objectively select which of several posologies is optimal for the
target individual. Essentially, this system allows, a prescribing
physician to conduct a double-blind clinical experiment on a single
user (see FIG. 5).
FIG. 5 shows the titration system including two stages: Stage 1:
the Titration Period and Stage 2: the Posology Period.
While the physician decides the posologies that will be tested and
compared (i.e., posology conditions), the system 100 handles the
randomizing, scheduling and interaction with a participating
pharmacy for example. As such, the present system's titration
module makes it possible, and very simple, to run a personalized
double blind clinical trial.
The titration system or process in FIG. 5 is as follows:
The physician has an interface or user's device 1 and the target
user has a patient's device. As such, both the physician and the
target user have the system's application and their accounts are
linked (see above), The target user's address is entered and
stored.
(Step A): The physician selects and designs a titration schedule
with the help of the system's algorithms.
More specifically and as shown in FIG. 4, the physician (or person
prescribing the medication) uses the system's application to
characterize the posology conditions that will be compared:
P1) posology variables, i.e., what vary from different posology
conditions, e.g., the type of drug and dosages.
P2) posology constants, i.e., what remains the same between
posology conditions, e.g., the time of day and frequency of drug
intake, directions such as taking the drug on an empty stomach.
P3) posology interval: the amount of time each posology condition
will last, and if there is a period of time in-between conditions
when no drugs are taken ("flush-out period").
The result of P1, P2 and P3 is a titration schedule. An example of
a titration schedule is:
P1--Three posology conditions, which differ in terms of dose and
drug type: 5 mg of Drug A, 10 mg of drug B & placebo (posology
variables).
P2--AU three posology conditions are taken once per day, in the
morning, on an empty stomach (posology constants).
P3--Each posology condition will be taken for 9 days, with 2 days
with no medication in between each 9 day period (posology
interval).
FIG. 4 illustrates how the posology variables and the posology
intervals comprise the final randomized titration schedule.
By selecting the maximum dose and the type of medication, the
system's algorithm suggests a number of ranked titration schedules
based on the outcome of previous titration schedules.
The physician then decides what data the target user should to
collect via their application (e.g., depression questionnaire once
per day in the evening). The system may suggest a number of
questionnaires or other data collection methods depending on the
medication that comprises the titration schedule. The target user
can have other users collect similar data (have their partner fill
out a daily mood questionnaire with regards to the target
user).
Once the titration schedule is set within the system's application
(Step B in FIG. 5), a prescription is printed via the application,
signed by the prescribing physician, or signed directly in the
application via an electronic signature. The prescription is sent
(via scan, picture or fax, or directly via an electronic signature)
to the pharmacy partner, which has access to the system. The actual
order of the posologies is randomized and known only by the
pharmacist(s) via their pharmacy account privileges (for example,
1st nine day period, I, is 10 mg; the 2nd nine day period, II, is 5
mg; and the final nine day period, III, is placebo--this
information in unknown to the user or the physician).
A titration schedule that has been randomized is called a
randomized titration schedule. The posologies are mailed by the
participating pharmacy or online pharmacy partner according to the
randomized titration schedule. Drug bottles are clearly marked I,
II, III, and the doses it may contain.
Therefore, the titration schedule is received, randomized and the
prescriptions corresponding to the posology conditions is shipped
to the target user. The target user's interface device 3 clearly
indicates from which bottle to take the medication from on a given
day and other details pertaining to the randomized titration
schedule-all the while keeping the actual posologies variables
secret (e.g., the actual type and dosage of each condition).
In step C, the system's application informs the user of the
titration schedule and prompts the various users (parent, teacher,
partner etc.) to complete evaluations that provide user-feedback.
Therefore, during the titration period, the user(s) is (are)
prompted to make evaluations and send user-feedback via the
application, as described above. For example, each day during the
titration schedule the system's application sends reminders and
prompts to the users to make behavioral and side effect evaluations
(using the feedback module).
In step D, after the titration period is ended, the parents meet
with the doctor, and the system's application generates graphs
illustrating differences between the posology conditions (using the
feedback module). Together, they select the posology conditions.
The physician may prompt a titration physician report, i.e.,
specific set of graphs based on the data collected via the feedback
module, that compares the advantages and disadvantages of each
posology period (I, II or III) to assist the physician (and the
user) in selecting which of the posology conditions was the best
for the target user. Once the best posology condition is selected
(e.g., II), the physician can prompt the application to reveal its
corresponding posology (e.g., II=5 mg of drug .alpha.). The
physician makes a normal prescription with the selected dosage. The
user can continue to use the same pharmacy partner used in the
titration system; the pharmacy may thus acquire a new long-term
customer.
In step E, the data generated during the titration period is fed
into the algorithm to improve its ability to suggest appropriate
titration schedules for future users, to suggest another titration
schedule with more precise doses with the same user, or as input
into third party software.
In another embodiment, the titration program consists of providing
a a base line with a target individual and utilizing a score or a
biosensor. Then comparing the score or the result of the biosensor
once the target individual has received the therapeutic
composition.
Integration with Other Software
As shown in step G of FIG. 3, the system's application can be used
in conjunction with third party software that uses an algorithm to
select treatment or posology variables. Using the features
described above (in particular evaluations, user-feedback and
analysis) the application may provide data to inform the software's
algorithm with real-life data to help determine the best course of
treatment action.
Communication System
The communication system serves as a communication platform where
the users exchange information, data, documents, messages and
information in an effort to coordinate the management or a medical
or mental health condition, including drug withdrawal.
The communication system described below has elements that are
similar to the posology system described above. While the posology
system is specific to finding an ideal posology, the communication
system deals with all aspects of treatment and management of the
condition, which may or may not include medication.
Accounts
The communication system is intended to help the target individual
(i.e., the person that is the object of the medical or mental
health intervention who may be medicated or not), relatives,
healthcare professionals, social works, lawyers, teachers and
others work together; because these individuals have different
objectives and needs, the application has specific account types.
For example, a target individual account displays information and
functionalities that is relevant to the target individual, while a
physician account provides those pertinent to a health care
professional. It is the target individual account that controls
what information is available to other accounts, via establishing
permissions. Other accounts include relative, teacher, social
worker, legal and psychologist accounts. If the target individual
is a minor, its legal guardian has control over the target
individual account via a parent account.
For example, FIG. 6 illustrates that accounts are associated with a
single individual, Billy, who is diagnosed with a condition,
C-Billy for example, across several devices. These devices include
interfaces that are in communication with the controller of the
system 10 or 100 and provide account platforms thereon to the
different users. For example, Jane is Billy's mother and she has a
parent account that has full access to Billy's dossier. Jane also
has a daughter Jennifer, who is diagnosed with a condition
C-Jennifer. Jane has full access to Jennifer's dossier t via Jane's
parent account. Jane can access her account via from her mobile and
her laptop for example. Mr. Adams is a teaches and he has
restricted access to Jennifer's dossier as well as to Alex's
dossier. As Billy's physician, Dr. Smith, has restricted access to
Billy's dossier via his physician account but as Alex's uncle, Dr.
Smith also has access to Alex's dossier via a family account. A
dossier is data is contained within the controller's database (i.e.
the server). A dossier comprises the data associated with a target
individual (e.g., Jane manages her son Billy's dossier via her
parent account; Dr. Smith manages Billy's dossier via her
physician's account). It is possible for an individual to have both
a parent and a physician's account (e.g., Dr. Smith's nephew was
diagnosed with a condition C-Alex; her nephew is her relative and
not her patient, thus she is linked to her nephew's account via a
relative (or family) account); at any time, the user can switch
between accounts via a Change Account function.
An account can be associated with one or more dossiers (a parent
may have several children; a physician typically has many patients.
FIG. 6 illustrates non-limiting examples of the relationship
between individuals, accounts and dossiers).
In an embodiment, the application's functionalities are
dossier-specific: before using the application, the target
individual in question must be selected, via the target individual
select screen. Every time the application is started, the target
individual select screen appears. The target individual select
screen is available at any time to switch between dossiers.
In an embodiment, the target individual has special privileges:
namely, the right to determine limits and permissions of other
accounts associated with the dossier. Moreover, all account links
(see below) between a dossier and other accounts must be approved
by the target individual account (e.g., Jane received a relative
link request from her neighbor, which she refused).
An account link is a set of permissions that allow an outside
account to have specific access, and to make specific changes, to a
dossier, specified by both the user of the target individual
account and what type of account is being linked (e.g., physician,
relative).
For example, Jane allowed Dr. Smith to link her physician's account
to her son Billy's dossier, giving Dr. Smith full access to medical
and symptom-related Information. The parent account holder can
modify permissions at any time.
The Linking Process
In an embodiment. In order to establish a link between two
accounts: --The user can locate an account using the account search
function: Name, Address, Account Type (relative, physician etc.).
If the individual that is being searched for (e.g., Dr. Smith) has
the appropriate account in the database (e.g., Dr. Smith has a
physician account), then the individual being searched for gets a
confirmation notification in their account module and a message in
their notification area (explained below). The individual that was
located for must then confirm the nature of the relationship (e.g.,
Dr. Smith must confirm, or deny, that Billy is her patient). Upon
confirmation, the accounts are now linked. If an account already
exists for the individual, missing information is updated (Dr.
Smith's office hours). The user can now click on the account icon
in the account module set permissions and modify information with
regards to this individual.
If the individual is not found by the account search function, the
user can choose to send an automated email asking the individual to
download the application and create an account. An unlinked account
representing the individual can be created, and information (e.g.,
address) can be entered by the user. Each account type corresponds
to a specific interface and default restrictions, some of which way
be modified by a parent account.
General Interface
In an embodiment, the Welcome Screen (see FIG. 10) has an icon for
each active module (Module icons). Clicking on the icon brings up
the module's main screen (e.g., the account icon leads to the main
account screen). Closing the module page brings back the Welcome
Screen (by pressing the home icon).
At the top is the Notification Area, a space dedicated to text
messages (welcome message, reminders, alerts, etc.). If more than
one message is required, the Module Icons are pushed down to make
space. Messages can be clicked to open the relevant module (a small
icon representing the relevant module appears on the message to
help the user build a cognitive map of the application's
architecture).
Module icons are dynamic (in their location and through time). Most
Module Icons are permanent, while others are time sensitive, e.g.,
for certain functions that are important to perform (e.g., connect
with your physician). Module Icons can appear at specific times (at
first, a minimal amount of Module Icons appear to minimize
cognitive overload).
Each module is assigned a rank (by the application designers) that
will determine its location within the Welcome Screen.
User-initiated customization options may be made available in later
versions.
Account Module
In en embodiment, the account module manages accounts and the
linked process between different accounts, notably:--search for
individuals using the account search function; --send contact
messages offering to link accounts; --send premade email offering
to download the application; --create/delete/edit account details;
--If the accounts are linked, establish permissions.
Feedback Module
In an embodiment, the Feedback is similar in principle to
evaluations found in the posology system, but of a much wider
scope. This section permits the user to collect data (typically
behavioral and cognitive) concerning the target individual from a
wide range of evaluation types. In addition, the presence of
interfering life events (emotionally disturbing events, illness,
etc.) is documented and a score is attributed to its perceived
impact. The nature of the evaluations and their deployment
schedules are specific to the account type (e.g., psychologists and
teachers have different evaluation objectives and schedules) and
the condition of the target individual (e.g., depression and
alcoholism require vastly different types of feedback).
The type and frequency of the requested feedback is dynamic and
changes as more data is collected and analyzed by the application
of system 100. Certain types of data trigger changes in the
frequency and nature of the requested feedback. Also, some users
may request specific feedback from other users (e.g., a teacher may
request that a parent take a weekly "emotional event
questionnaire").
Most types of feedback evaluations are optional and schedulable
(e.g., if the parents accept the teacher's proposal, they can
schedule the "emotional event questionnaire" to each Friday, with
the help of the application's calendar function). The application
will prompt the user to initiate or complete the questionnaire
(e.g., each Friday a notification appears on the parent's interface
device reminding them that the evaluation is scheduled for today),
and continues to send reminders if they fall to complete the
evaluation in time.
The evaluations are all preformed on the user-device, and consist
of questionnaires, computerized tests (e.g., neurocognitive
evaluations) any other means of collecting data that pertains to
the efficiency or negative effects of the medication, end to the
state of the condition for which medication is being prescribed. It
is possible for the target individual make self-evaluations.
Some Tests Include:
Medication/Prescription Module:--finked to the posology system,
indicating the posology to the users as determined by the system;
Create/delete/edit information relating to the prescription.
Questionnaires:--Questionnaires relating to the target-individual's
medical or mental health condition are presented.
Medical measurements:--create/delete/edit information relating to
medical measurements: height, weight, blood pressure and heart
rate; --enables to place a child's measurement within a height and
weight graph (usual growth cuives).
Digitalized neurocognitive and psychological tests:--to be done
directly on the device (for example, digitalized version of a
working memory test, sustained attention tests, etc.)
Open ended questions: --Audio recordings or text based-form can be
used to answer open ended questions (e.g., how do you feel
today?).
Biosensor feedback:--Biosensor-based devices, such as wearables,
can send heart rate, respiration and skin conductance data (and
many other physiological measurements) into the target individual's
device that is in turn sent to the server for analysis (for
example, heart rate, blood pressure and respiration data taken from
a wearable biosensor watch will provide pertinent data for someone
with an anxiety disorder).
Feedback Prompts
As the posology system described above, the system's application
sends reminders (email and in-device prompts such as
push-notification and pop-ups) to initialize or complete specific
feedback-related activities that are past due date. These
feedback-prompts are associated with a calendar within the system's
application, or synced with a third-party calendar (e.g., Google
calendar).
Analysis of Data
Data analyses operate on the same principles as the posology
system, but treat a much wider range of data (e.g., includes
medical measurements), and potentially for a much longer time
period (years end decades). Data collected from feedback-related
activities are sent to the server. The data is analysed by an
algorithm along with the data from other users concerning the same
target individual. These analyses may modify the feedback schedules
of the users, i.e., the type and frequency of feedback that is
required by the application (via feedback prompts) for each user.
Feedback schedules dependent on the condition and the user. For
example, a psychologist may be asked by the application to fill out
a specific evaluation during each visit from the target
individual.
In an embodiment, the application's analysis of the feedback data
also prompts messages or alerts to specific users. For example,
FIGS. 7 and 8 depict user-application interactions in the
communication system.
In FIG. 7, a teacher via their User A Device interface accesses
their account and is prompted (1) by the system for daily classroom
behavior evaluation and in response provides data (2) indicating
that a child is getting more aggressive at school (from data
derived from a daily classroom behavior evaluation), both the
linked parent (via their User B interface) and psychologist
accounts are automatically notified (4) of this rise in aggression,
along with a graph illustrating the trend. In this example, the
parent and psychologists can then annotate the notification (5) and
leave a comment via the notepad function. In addition, the
application automatically adds two more aggression-related
questions (3) to the daily classroom behavior evaluation to be
completed by the teacher. To continue with this example, the
psychologist requests that the parent account holder take a
retrospective emotional stability questionnaire once a week; the
parent accepts this request, and schedules this Friday at 3:00
PM.
In FIG. 8, a targeted individual is prompted (1) via their account
accessed by their User A Device regarding their weekly depression
evaluation. The targeted individual provides data (2) indicating
increased dysphonic mood. The targeted individual also provides
biosensor data (3) demonstrating reduced mobility. The Individual's
psychologist received data from the individual's feedback to the
server. This Information may be included in a report the
psychologist will generate. The psychologist then provides via
their User B Device to their account their assessment. In this
example, the psychologist decides to increase the frequency of the
depression evaluation frm weekly to 3 times a week, and adds two
questions evaluating suicide idealization (elevated scored on these
questions prompts an alert push notification on the psychologist's
User B Device).
Generate Reports
Like the posology system, the reports present data in tables, bar
graphs, line graphs and pie charts, but of a much wider range of
formats and data choices. Many premade report types are specific to
the account type and condition (e.g., a premade report specifically
made for psychologists that are following a target individual that
is diagnosed with depression).
Steps for generating reports include:--Select Predetermined Report
Structures (e.g., side effect end symptom report for physician;
monthly progress report for parents); --Select time scale (e.g.,
provides several options such as 7 days; 7 weeks; 7 months; custom
time scales would be useful but perhaps in a later version.)
When prompted by the user, the collected data is analyzed and
presented in a report form on the device. The application selects,
synthesizes, summarizes end produces statistical analyses with the
data collected from user evaluations; this information is presented
in a concise manner by means of tables, end graphical
representations such as bar graphs, line graphs and pie charts,
depending on the type of report structure that is selected and its
intended viewer. The report content and structure is
customizable.
Progress reports for target individual's whose physician does not
have an account: --These options are made available make a
physician aware of the application. --The time scale of the report,
and its form, may depend on the amount of data collected. --Email
standard report to physician (enter email). --Email standard report
to self (to allow to open and print report on a computer, to bring
during visit). --Display standard report on screen, to show
physician during visit.
Other application functionalities: --A calendar function
(Integrated with the apple/Google calendar). --Standard reminders
and alerts (push notifications). The system may send emails
inviting different accounts (e.g., relatives) to download the
application (if unlinked) or to complete questionnaires. Reminders
are sent after a certain period of time, and for a limited
duration. --A drop box to upload, download, delete, tag and flag a
document (.doc, .docx, .pdf). Tags are simply key words that can be
searched. Flags are tags that are specific to a level of
importance. Other accounts can be tagged (that prompts a
notification message in the other account's Notification Area). The
ability to create folders to organize files would be useful, but
can wait for a subsequent application version. --A notepad to write
notes in a private manner or a message that is shared with a
specific account(s).
Other Uses for the Application
The Use of the System for Withdrawal
The posology and communication systems can function in the context
of drug withdrawal (over the counter, prescription, recreational
and illegal drugs). In this manner, the posology system can adjust
the drug withdrawal schedule (the timing of the diminishment of the
dosage of a drug) in relation to the user feedback that is
collected by the application, analyzed by the system and displayed
to the prescribing physician. Similarly, the communication system
assists the target individual and concerned actors in collecting
and sharing data that will guide decisions impacting the drug
withdrawal process.
The Use of the System for Clinical Studies
The posology end communication systems can equally function in the
context of clinical or other research studies. On possibility is to
use the titration system to establish optimal doses for a drug
under study, such as recommended doses, maximum and minimum doses
for the general public. This can be done in order to establish
optimal doses for the general population and to determine optimal
doses with relationship to a particular user variable, such as
weight, age, sex, or a combination of variables. In particular, the
system's ability to prompt user feedback via a mobile device allows
the possibility to study the effects of a drug in the participant's
everyday life with minimal intervention; because the feedback
(questionnaire, neurocognitive testing, biosensor data, etc.) is
collected in the participant's normal setting, the research profits
from a high level of ecological validity.
Use of Other Data Types
The application also makes use of data that is taken from other
means, such as diagnostic tests measuring an individual's genotype
information, salivary analysis (e.g., levels of neurosteriods or
specific proteins), blood sample analysis (e.g., levels of
cortisol), EEG analysis (e.g., alpha to theta ratio in the frontal
cortex) and the like.
Predicting Treatment Outcomes
Through assisted machine learning and data mining, the application
reveals patterns and trends within the date that will be used to
predict treatment outcomes and will serve as a basis for treatment
recommendations, including posology-related recommendations for
both the pharmacokinetic and titration modules.
FIG. 9 exemplifies the use of data to predict treatment outcomes. A
machine learning circuit is provided between pre-treatment
evaluations and treatment evaluations/feedback. The pre-treatment
evaluations include neurophysiological data, biological data,
behavioral data, cognitive data. This information is then data
mined along with real-time treatment evaluations and user feedback
related to neurophysiological data, biological data, behavioral
data, cognitive data. Therefore, data mining and machine learning
is based on individual and group data fed from real-time treatment
evaluations and pre-treatment evaluations and on that basis (as
explained above for system 10) provides outcome predictions and
treatment recommendations and thus provides a treatment
administration which is refed back into the system for further date
mining and machine learning.
In an embodiment, the user feedback described herein comprises
behavioral symptoms.
In an embodiment, the present system and method and its various
embodiments ca also be used in clinical studies for data collection
of patients in order to assist in maximizing posology
evaluation.
In an embodiment, the present system and method provide an
educational tool for educating a patient with respect to their
pharmacodynamics.
The various features described herein can be combined in a variety
of ways within the context of the present disclosure so as to
provide still other embodiments. As such, the embodiments are not
mutually exclusive. Moreover, the embodiments discussed herein need
not include all of the features and elements Illustrated and/or
described and thus partial combinations of features can also be
contemplated. Furthermore, embodiments with less features than
those described can also be contemplated. It is to be understood
that the present disclosure is not limited in its application to
the details of construction and parts illustrated in the
accompanying drawings and described hereinabove. The disclosure is
capable of other embodiments and of being practiced in various
ways. It is also to be understood that the phraseology or
terminology used herein is for the purpose of description and not
limitation. Hence, although the present disclosure has been
provided hereinabove by way of non-restrictive Illustrative
embodiments thereof, it can be modified, without departing from the
scope, spirit and nature thereof and of the appended claims.
* * * * *